Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Phytoliths from soil surfaces and water reservoirs of the Brazilian semi-arid Caatinga

International audienceUnderstanding processes that explain phytolith assemblages and their concentration in the soil and sediments is essential to interpret long-term ecological changes. The present study shows modern phytolith assemblages and their relationship with the vegetation cover in the Caatinga biome, state of Ceará, northeastern Brazil. We collected recent samples from soil surfaces in 2015 in three different vegetation covers (open shrubby Caatinga, dense shrubby Caatinga, and Caatinga forest vegetation) and from two cores with recently deposited sediments, in Massapê (74 cm depth, 55 years old) and Varjota (50 cm depth, 58 years old). After phytolith extraction, we studied twelve samples from soils and thirteen samples from cores. The counting revealed 26 different phytolith types with some differences in the concentration of silica bodies in different vegetation covers. Phytoliths were well preserved and could be related to the presence of plants, such as Poaceae (subfamily Panicoideae and Chloridoideae), Arecaceae and Cyperaceae. In the two cores, the most common type was globular echinate and could be associated with the presence of palms in the riparian forest and open landscapes in the surroundings. In all soil surfaces samples, the saddle was the most frequent type. We found a high concentration of globular granulate around Aiuaba Ecological Station, which harbors a preserved Caatinga area. Finally, we observed a good match between phytolith assemblages and the vegetation cover collected in soils and reservoirs

When archives are missing, deciphering the effects of public policies and climate variability on the Brazilian semi-arid region using sediment core studies

International audienceThe northeastern region of Brazil is the most densily populated and biodiverse semi-arid regions of the planet. Effects of the natural climate variability and colonization on the landscape have been described since the beginning of the 16 th century but little is known about their effects on natural resources. Climate projections predict temperatures above 40 °C and an increase in the number and duration of droughts at the end of the 21 st century with strong societal impacts. Here, we analyze the influence of public policies, human activities and natural climate variability on the environment over the last 60 years. Our study is based on sedimentological and environmental reconstructions from two sediment cores collected in two dam lakes on the river Acaraú in the State of Ceará. Multiproxy analyses of both cores (inorganic geochemistry, pollen, charcoal, remote sensing) at an annual resolution showed that 1) at interannual scale composition and distribution of the dry forest (known as Caatinga) were not affected by the alternance of drought and high moisture episodes; 2) at decadal scale human activities such as agriculture were reflected by changes in vegetation cover and fishery by progressive changes in lake trophic status; 3) public policies were able to promote changes in the landscape e.g., land colonization with the regression of the dry forest and irrigation plan able to amplify the deforestation and change the floristic composition. Thanks to paleoscience approach, our environmental diagnosis should help future decision-making and provide guidelines for preservation of resources and wellbeing of the inhabitants